EncodeAgent Daily Digest #2

Exploring The Vexing Question Of Whether Generative AI Can Perform Mental Health Reasoning Or Maybe The Psychological Advice Generated Is Just Mindless Mimicry

https://www.forbes.com/sites/lanceeliot/2024/02/14/exploring-the-vexing-question-of-whether-generative-ai-can-perform-mental-health-reasoning-or-maybe-the-psychological-advice-generated-is-just-mindless-mimicry/?sh=362c65f56c71

In this column, the author continues to explore the impact of generative AI on mental health therapy, focusing on whether AI can perform mental health reasoning. The author emphasizes the complexity of the term “mental health reasoning” and the need for caution when applying it to AI. The discussion includes an analysis of a dialogue with generative AI about ADHD and the widespread use of AI for mental health advice, highlighting the ethical and legal implications. The author critiques the anthropomorphizing of AI through language that implies understanding, thinking, or reasoning, arguing that AI is not sentient and such claims are misleading. The column also reviews a research study that evaluates the performance of various large language models (LLMs) on mental health prediction tasks using online text data. The study shows that instruction fine-tuning can significantly improve LLMs’ performance on mental health tasks and explores their capability for mental health reasoning. The author conducts an experiment using ChatGPT to assess two sample social media posts for stress and provides mental health recommendations, demonstrating how generative AI can be nudged in a desired direction. The column concludes by questioning whether the stepwise explanations provided by AI are genuine reasoning or merely a form of mimicry based on pattern matching, leaving readers to ponder the true nature of AI’s capabilities in mental health reasoning.

How Financial Services Firms Can Build A Generative AI Assistant

https://www.forbes.com/sites/granteasterbrook/2024/02/14/how-financial-services-firms-can-build-a-generative-ai-assistant/?sh=779588aa77a0

The article discusses the emergence of generative AI assistants in financial services and outlines three main approaches for firms to build such assistants: using an off-the-shelf API from external vendors like Amazon or OpenAI, deploying an open-source large language model (LLM) like Meta’s Llama 2, or developing a proprietary LLM, which is the most expensive and complex option. It emphasizes the importance of the AI assistant’s conversational capabilities, low latency, and the implementation of proper guardrails, especially concerning client data and sensitive topics like financial advice. The article highlights the regulatory challenges and the need for compliance with privacy and data protection rules. It also suggests that financial services firms should leverage generative AI to create unique value propositions for clients, as exemplified by Public’s Alpha assistant, which provides high-level stock research. The article serves as a starting point for firms considering the development of a generative AI assistant, encouraging them to think about how such technology can enhance client experience and differentiate their services.

Startups vs Incumbents in the AI Era

https://www.nfx.com/post/startups-vs-incumbents-ai

The article discusses the potential impact of Generative AI on the tech industry, suggesting that while incumbents like Amazon, Microsoft, and Google have seen significant stock increases and are investing heavily in AI, the ultimate beneficiaries of the AI revolution could be either established companies or agile startups. It emphasizes the importance of distinguishing between disruptive and sustaining innovations, with the former creating new markets and the latter improving existing ones. Examples of disruptive innovations include Uber and Lyft in the taxi industry and the iPhone in the mobile market. The article advises startups to consider whether their AI technology is truly disruptive, offering a fundamentally new capability or a hard-to-replicate advantage. It suggests targeting markets where incumbents are slow to adopt new technologies, offering solutions that are significantly better than existing ones, and exploiting business model conflicts where AI undermines incumbents’ revenue streams. For sustaining innovations, startups should focus on niches that incumbents overlook or cannot serve. The article cites the chess industry’s growth due to AI as an example of how technology can expand an industry rather than dominate it. It concludes by urging startups to have a clear vision and to understand their position in the market, whether they are offering a truly disruptive innovation or a sustaining one within an industry dominated by strong incumbents.